Introduction: Vector databases are the backbone of modern AI applications—powering semantic search, RAG systems, and recommendation engines. But as your vector collection grows from thousands to millions of embeddings, naive approaches break down. Query latency spikes, memory costs explode, and recall accuracy degrades. This guide covers practical optimization strategies: choosing the right index type for […]
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